August 2019 CIS Magizing cover

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications, in keeping with the Field of Interest of the IEEE Computational Intelligence Society (IEEE/CIS). Additionally, CIM serves as a media of communications between the governing body and its membership of IEEE/CIS. Authors are encouraged to submit papers on applications oriented developments, successful industrial implementations, design tools, technology reviews, computational intelligence education, and applied research.

Contributions should contain novel and previously unpublished material. The novelty will usually lie in original concepts, results, techniques, observations, hardware/software implementations, or applications, but may also provide syntheses or new insights into previously reported research. Surveys and expository submissions are also welcome. In general, material which has been previously copyrighted, published or accepted for publication will not be considered for publication; however, prior preliminary or abbreviated publication of the material shall not preclude publication in this journal.


Impact Score


Journal Citation Metrics Journal Citation Metrics such as Impact Factor, Eigenfactor Score™ and Article Influence Score™ are available where applicable. Each year, Journal Citation Reports© (JCR) from Thomson Reuters examines the influence and impact of scholarly research journals. JCR reveals the relationship between citing and cited journals, offering a systematic, objective means to evaluate the world's leading journals.  Find out more about IEEE Journal Rankings.


IEEE CIM Fast Track on "Computational Intelligence for Combating COVID-19"

In order to support the world-wide efforts in fighting COVID-19, the IEEE Computational Intelligence Society (IEEE CIS) has set up a program, the COVID-19 Initiative. Under this initiative, the IEEE CIM will expedite, to the extent possible, the processing of all articles submitted to this program with primary focus on COVID-19.


Current Special Issues

  • IEEE CIM Special Issue on "Knowledge Transfer in Evolutionary Optimization," Guest Editors: Liang Feng, Handing Wang, Yaochu Jin, Kay Chen Tan. Submission Deadline: March 1, 2021. (Call for Papers)

  • IEEE CIM Special Issue on "Explainable and Trustworthy Artificial Intelligence," Guest Editors: José María Alonso, Corrado Mencar, Hisao Ishibuchi. Submission Deadline: February 22, 2021. (Call for Papers)


Featured Paper

Ant Colony Optimization Algorithms for Dynamic Optimization: A Case Study of the Dynamic Travelling Salesperson Problem
Authors: Michalis Mavrovouniotis, Shengxiang Yang, Mien Van, Changhe Li, Marios Polycarpou
Publication: IEEE Computational Intelligence Magazine (CIM)
Issue: Volume 15, Issue 1 – February 2020
Pages: 52-63

Abstract: Ant colony optimization is a swarm intelligence metaheuristic inspired by the foraging behavior of some ant species. Ant colony optimization has been successfully applied to challenging optimization problems. This article investigates existing ant colony optimization algorithms specifically designed for combinatorial optimization problems with a dynamic environment. The investigated algorithms are classified into two frameworks: evaporation-based and population-based. A case study of using these algorithms to solve the dynamic traveling salesperson problem is described. Experiments are systematically conducted using a proposed dynamic benchmark framework to analyze the effect of important ant colony optimization features on numerous test cases. Different performance measures are used to evaluate the adaptation capabilities of the investigated algorithms, indicating which features are the most important when designing ant colony optimization algorithms in dynamic environments.

Index Terms: Heuristic algorithms, Ant colony optimization, Classification algorithms, Particle swarm optimization, Benchmark testing
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